Epoch: 0001 train_loss= 2.08903 train_acc= 0.12453 val_loss= 2.07521 val_acc= 0.10345 time= 0.46648
Epoch: 0002 train_loss= 2.08434 train_acc= 0.12075 val_loss= 2.07557 val_acc= 0.10345 time= 0.00000
Epoch: 0003 train_loss= 2.08035 train_acc= 0.12830 val_loss= 2.07629 val_acc= 0.10345 time= 0.01562
Epoch: 0004 train_loss= 2.07702 train_acc= 0.15094 val_loss= 2.07713 val_acc= 0.10345 time= 0.00000
Epoch: 0005 train_loss= 2.07187 train_acc= 0.12075 val_loss= 2.07798 val_acc= 0.10345 time= 0.00000
Epoch: 0006 train_loss= 2.06888 train_acc= 0.16226 val_loss= 2.07886 val_acc= 0.17241 time= 0.01563
Epoch: 0007 train_loss= 2.06506 train_acc= 0.14340 val_loss= 2.07975 val_acc= 0.17241 time= 0.00000
Epoch: 0008 train_loss= 2.06311 train_acc= 0.15472 val_loss= 2.08065 val_acc= 0.17241 time= 0.00000
Epoch: 0009 train_loss= 2.06147 train_acc= 0.13962 val_loss= 2.08160 val_acc= 0.17241 time= 0.01563
Epoch: 0010 train_loss= 2.06061 train_acc= 0.13585 val_loss= 2.08255 val_acc= 0.13793 time= 0.00000
Epoch: 0011 train_loss= 2.05804 train_acc= 0.14340 val_loss= 2.08355 val_acc= 0.13793 time= 0.00000
Epoch: 0012 train_loss= 2.05836 train_acc= 0.16226 val_loss= 2.08440 val_acc= 0.10345 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 2.05051 accuracy= 0.20339 time= 0.00000 
